Apparatuses, Computer-Implemented Methods, and Computer Program Products for Automatic Product Verification and Shelf Product Gap Analysis

1. An apparatus for automatic product verification and shelf product gap analysis, the apparatus comprising: a multi-imager imaging engine comprising at least a near-field imager and a far-field imager, the near-field imager associated with a near field of view that is wider than a far field of view associated with the far-field imager; at least one processor; and at least one non-transitory memory having computer-coded instructions stored thereon, wherein the computer-coded instructions, in execution with the at least one processor, configures the apparatus to: capture at least one far-field image data object using the far-field imager; identify product label information associated with a product label represented within the at least one far-field image data object, wherein the product label represented within the at least one far-field image data object comprises one or more visual indicia; detect the one or more visual indicia in the far-field image data object; attempt to decode the one or more visual indicia to identify at least a portion of the product label information; in response to successfully decoding the one or more visual indicia in the at least one far-field image data object, capture a near-field image data object using the near-field imager; identify a product set represented within the near-field image data object; determine whether at least one product gap is identified between a first product of the product set and a second product of the product set; and generate at least one alert based on identification of the at least one product gap.

2. The apparatus of claim 1, wherein to determine whether at least one product gap is identified between the first product of the product set and the second product of the product set, the apparatus is further configured to: identify product dimension data for at least the first product of the product set; process the near-field image data object to identify a defined empty space between the first product and the second product; and determine, based on the defined empty space and the product dimension data for at least the first product, the product gap between the first product and the second product.

3. The apparatus of claim 1, wherein to identify the product set represented within the near-field image data object, the apparatus is further configured to: apply the near-field image data object to a trained product detection artificial intelligence algorithm or at least one trained product detection machine learning model.

4. The apparatus of claim 1, wherein to identify the product label information associated with the product label, the apparatus is further configured to: apply the far-field image data object to at least one trained OCR machine learning model, wherein the trained OCR machine learning model is configured to output at least a portion of the product label information.

5. The apparatus of claim 1, the apparatus further configured to: select the at least one alert from the group of a product mismatch alert, a price mismatch alert, and a product gap alert.

6. The apparatus of claim 1, the apparatus further configured to: cause storing of at least one image data object to at least one image datastore associated with training a product detection artificial intelligence algorithm or a trained product detection machine learning model.

7. The apparatus of claim 1, the apparatus further configured to: cause rendering of an interface to a display associated with the apparatus based on at least (1) the determination of whether the product label information matches the expected product label information associated with the product label, or (2) the determination of whether the product set includes at least one incorrect product based on the product label.

8. The apparatus of claim 1, wherein to determine whether the product label information matches the expected product label information associated with the product label, the apparatus is further configured to: retrieve the expected product label information based on at least product identification information from the product label information; compare at least a portion of the product label information with the expected product label information to generate label comparison results data; and determine whether the product label information matches the expected product label information based on the label comparison results data.

9. The apparatus of claim 1, wherein the portion of the product label information comprises a first portion of the product label information, the apparatus further configured to: retrieve, from at least one datastore, a second portion of the product label information based on at least product identification information in the first portion of the product label information.

10. A computer-implemented method for automatic product verification and shelf product gap analysis, the computer-implemented method comprising: capturing at least one far-field image data object using a far-field imager of a multi-imager imaging engine; identifying product label information associated with a product label represented within the at least one far-field image data object, wherein the product label represented within the at least one far-field image data object comprises one or more visual indicia; detecting the one or more visual indicia in the far-field image data object; attempting to decode the one or more visual indicia to identify at least a portion of the product label information; in response to successfully decoding the one or more visual indicia in the at least one far-field image data object, capturing a near-field image data object using a near-field imager of a multi-imager imaging engine; identifying a product set represented within the near-field image data object; determining whether at least one product gap is identified between a first product of the product set and a second product of the product set; and generating at least one alert based on identification of the at least one product gap.

11. The computer-implemented method of claim 10, wherein identifying at least one product gap between the first product of the product set and the second product of the product set comprises: identifying product dimension data for at least the first product of the product set; processing the near-field image data object to identify a defined empty space between the first product and the second product; and determining, based on the defined empty space and the product dimension data for at least the first product, the product gap between the first product and the second product.

12. The computer-implemented method of claim 10, wherein identifying the product set represented within the near-field image data object comprises: applying the near-field image data object to a trained product detection artificial intelligence algorithm or at least one trained product detection machine learning model.

13. The computer-implemented method of claim 10, wherein identifying the product label information associated with the product label comprises: applying the far-field image data object to at least one trained OCR machine learning model, wherein the trained OCR machine learning model is configured to output at least a portion of the product label information.

14. The computer-implemented method of claim 10, the computer-implemented method further comprising: causing rendering of an interface to a display based on at least (1) the determination of whether the product label information matches the expected product label information associated with the product label, or (2) the determination of whether the product set includes at least one incorrect product based on the product label.

15. The computer-implemented method of claim 10, wherein determining whether the product label information matches the expected product label information associated with the product label comprises: retrieving the expected product label information based on at least product identification information from the product label information; comparing at least a portion of the product label information with the expected product label information to generate label comparison results data; and determining whether the product label information matches the expected product label information based on the label comparison results data.

16. The computer-implemented method of claim 10, the computer-implemented method further comprising: causing storing of at least one image data object to at least one image datastore with training a product detection machine learning model.

17. The computer-implemented method of claim 10, wherein the portion of the product label information comprises a first portion of the product label information, the computer-implemented method further comprising: retrieving, from at least one datastore, a second portion of the product label information based on at least product identification information in the first portion of the product label information.